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Simple and Accurate Border Detection Algorithm for Melanoma Computer Aided Diagnosis

DEI—Department of Electrics and Information Engineering, Politecnico di Bari, 70125 Bari, Italy
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Diagnostics 2020, 10(6), 423; https://doi.org/10.3390/diagnostics10060423
Received: 5 May 2020 / Revised: 9 June 2020 / Accepted: 18 June 2020 / Published: 22 June 2020
(This article belongs to the Section Medical Imaging)
The interest of the scientific community for computer aided skin lesion analysis and characterization has been increased during the last years for the growing incidence of melanoma among cancerous pathologies. The detection of melanoma in its early stage is essential for prognosis improvement and for guaranteeing a high five-year relative survival rate of patients. The clinical diagnosis of skin lesions is challenging and not trivial since it depends on human vision and physician experience and expertise. Therefore, a computer method that makes an accurate extraction of important details of skin lesion image can assist dermatologists in cancer detection. In particular, the border detection is a critical computer vision issue owing to the wide range of lesion shapes, sizes, colours and skin texture types. In this paper, an automatic and effective pigmented skin lesion segmentation method in dermoscopic image is presented. The proposed procedure is adopted to extract a mask of the lesion region without the adoption of other signal processing procedures for image improvement. A quantitative experimental evaluation has been performed on a publicly available database. The achieved results show the method validity and its high robustness towards irregular boundaries, smooth transition between lesion and skin, noise and artifact presence. View Full-Text
Keywords: automatic segmentation; edge detection; melanoma; skin lesion; computer aided detection and diagnosis; skin cancer automatic segmentation; edge detection; melanoma; skin lesion; computer aided detection and diagnosis; skin cancer
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Guaragnella, C.; Rizzi, M. Simple and Accurate Border Detection Algorithm for Melanoma Computer Aided Diagnosis. Diagnostics 2020, 10, 423.

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